# Importing required libraries
import pandas as pd
import numpy as np
import math
import matplotlib.pyplot as plt
import hvplot.pandas
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler, OneHotEncoder
from sklearn.metrics import accuracy_score,confusion_matrix, classification_report

# Read the table
data=pd.read_csv("../Resources/Project_Indicators_Wide.csv")
data.head()

	country_code	country	region	status	year	ALC	AMR	BCG	CANP	DTP	...	HDI	HE	INCI	INFMR	LE	MCV	OBP	POPD	POPG	SR
0	ABW	Aruba	Latin America & Caribbean	NaN	2000	NaN	112.4760	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	73.787	NaN	NaN	504.811111	2.064841	93.3
1	ABW	Aruba	Latin America & Caribbean	NaN	2001	NaN	111.9155	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	73.853	NaN	NaN	516.066667	2.205163	93.3
2	ABW	Aruba	Latin America & Caribbean	NaN	2002	NaN	111.3550	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	73.937	NaN	NaN	527.733333	2.235515	93.3
3	ABW	Aruba	Latin America & Caribbean	NaN	2003	NaN	109.9290	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	74.038	NaN	NaN	538.977778	2.108324	93.3
4	ABW	Aruba	Latin America & Caribbean	NaN	2004	NaN	108.5030	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	74.156	NaN	NaN	548.577778	1.765473	93.3

5 rows × 23 columns

# Dropping NA's
df=data.dropna()
df.head()

	country_code	country	region	status	year	ALC	AMR	BCG	CANP	DTP	...	HDI	HE	INCI	INFMR	LE	MCV	OBP	POPD	POPG	SR
20	AFG	Afghanistan	South Asia	Developing	2000	0.002667	310.8305	30.0	0.502297	48.0	...	0.350	9.0	0.333	90.5	55.841	27.0	2.3	31.829117	2.975057	105.9
21	AFG	Afghanistan	South Asia	Developing	2001	0.005333	304.8580	43.0	0.508536	59.0	...	0.353	9.0	0.318	87.9	56.308	37.0	2.4	33.095904	3.902805	105.9
22	AFG	Afghanistan	South Asia	Developing	2002	0.008000	298.8855	46.0	0.512110	62.0	...	0.384	9.0	0.386	85.3	56.784	35.0	2.6	34.618102	4.496719	105.9
23	AFG	Afghanistan	South Asia	Developing	2003	0.010667	292.0365	44.0	0.515965	66.0	...	0.393	9.0	0.391	82.7	57.271	39.0	2.7	36.272510	4.668344	105.9
24	AFG	Afghanistan	South Asia	Developing	2004	0.013333	285.1880	51.0	0.520604	72.0	...	0.409	10.0	0.389	80.0	57.772	48.0	2.9	37.874413	4.321560	105.9

5 rows × 23 columns

df.dtypes

country_code     object
country          object
region           object
status           object
year              int64
ALC             float64
AMR             float64
BCG             float64
CANP            float64
DTP             float64
EDI             float64
GDP             float64
GDPG            float64
HDI             float64
HE              float64
INCI            float64
INFMR           float64
LE              float64
MCV             float64
OBP             float64
POPD            float64
POPG            float64
SR              float64
dtype: object

# Dropping unwanted columns
select_df=df.drop(columns=["country_code","country","year","status"])
select_df.head()

	region	ALC	AMR	BCG	CANP	DTP	EDI	GDP	GDPG	HDI	HE	INCI	INFMR	LE	MCV	OBP	POPD	POPG	SR
20	South Asia	0.002667	310.8305	30.0	0.502297	48.0	0.235	4.055180e+09	3.868380	0.350	9.0	0.333	90.5	55.841	27.0	2.3	31.829117	2.975057	105.9
21	South Asia	0.005333	304.8580	43.0	0.508536	59.0	0.247	4.055180e+09	3.868380	0.353	9.0	0.318	87.9	56.308	37.0	2.4	33.095904	3.902805	105.9
22	South Asia	0.008000	298.8855	46.0	0.512110	62.0	0.259	4.055180e+09	3.868380	0.384	9.0	0.386	85.3	56.784	35.0	2.6	34.618102	4.496719	105.9
23	South Asia	0.010667	292.0365	44.0	0.515965	66.0	0.271	4.515559e+09	3.868380	0.393	9.0	0.391	82.7	57.271	39.0	2.7	36.272510	4.668344	105.9
24	South Asia	0.013333	285.1880	51.0	0.520604	72.0	0.302	5.226779e+09	-2.875203	0.409	10.0	0.389	80.0	57.772	48.0	2.9	37.874413	4.321560	105.9

# Scatterplots comparing features with life expectancy
fig, axes = plt.subplots(6, 3, figsize=(15, 15))
fig.subplots_adjust(hspace=0.4, wspace=0.4)
for x,column in enumerate(select_df.columns.difference(['region'])):
    i=math.floor(x/3)
    j=x%3
    plot=sns.scatterplot(ax=axes[i,j],x=select_df['LE'], y= select_df[column],data=select_df,hue=select_df['region'])
    handles, labels = axes[0,0].get_legend_handles_labels()  
    axes[i,j].get_legend().remove()
fig.legend(handles, labels, loc = 'upper left',title="Regions")
plt.savefig("../Images/Plots/Scatter_plot.png")

# Distribution plot for life expectancy
fig, ax = plt.subplots(figsize=(15, 15))
sns.histplot(data=select_df,x='LE',hue='region',element='step',stat='density',kde=True)
ax.set_xlabel("Life Expectancy")
sns.move_legend(ax,'upper left', title="Regions")
plt.savefig("../Images/Plots/Distribution_plot.png")

# Correlation matrix
fig, ax = plt.subplots(figsize=(15, 15))
sns.heatmap(select_df.corr(),cmap='coolwarm',annot=True)
plt.savefig("../Images/Plots/Correlation_matrix.png")

Multiple_Linear_Regression

Multiple Linear Regression

Model to predict life expectancy

# Defining features and target
y=select_df["LE"]
X=select_df.drop(columns=["LE","region","ALC","BCG","DTP","GDPG","POPD","SR"])

# Splitting the data
X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=42)

# Normalize the data
X_scaler=MinMaxScaler().fit(X_train)
X_train_scaled=X_scaler.transform(X_train)
X_test_scaled=X_scaler.transform(X_test)

# Fitting the model
from sklearn.linear_model import LinearRegression

lm=LinearRegression()
lm.fit(X_train_scaled,y_train)

LinearRegression()

# Predicting the life expectancy values
y_pred=lm.predict(X_test_scaled)

# Performance of the model

from sklearn.metrics import r2_score,mean_squared_error

print (f"The coefficient of determination is {r2_score(y_test,y_pred):.3f}")
print (f"The mean squared error is {mean_squared_error(y_test,y_pred):.3f}")

The coefficient of determination is 0.995
The mean squared error is 0.462

# Training and Testing scores

training_score=lm.score(X_train_scaled,y_train)
testing_score=lm.score(X_test_scaled,y_test)

print(f"Training Score: {training_score:.3f}")
print(f"Testing Score: {testing_score:.3f}")

Training Score: 0.994
Testing Score: 0.995

# Plotting the residuals
plt.scatter(y_pred,y_pred-y_test)
plt.hlines(y=0,xmin=y_pred.min(),xmax=y_pred.max())
plt.xlabel("Predicted value")
plt.ylabel("Residuals")

Text(0, 0.5, 'Residuals')

# Calculating the intercept

print(f"The intercept is {lm.intercept_:.3f}")

The intercept is 58.169

# Calculating the coefficients

print("The coefficients are:")

for item in zip(X.columns,lm.coef_):
    print(item)

The coefficients are:
('AMR', -8.195918557413135)
('CANP', 0.385422279500121)
('EDI', -45.67680881174396)
('GDP', -0.1613350050110072)
('HDI', 99.66561814157986)
('HE', 0.6816497256898723)
('INCI', -34.357711285092385)
('INFMR', -5.0999163854464165)
('MCV', 0.13247233027562352)
('OBP', -0.47133514317458614)
('POPG', 0.4122633830283033)

Logistic Regression

Model to predict country's status

# Defining feature and target
y_s=df["status"]
X_s=df.drop(columns=["country","year","status","region","country_code"])

# Splitting the data
X_s_train,X_s_test,y_s_train,y_s_test=train_test_split(X_s,y_s,random_state=42)

# Standardizing the data
X_scaler=MinMaxScaler().fit(X_s_train)
X_s_train_scaled=X_scaler.transform(X_s_train)
X_s_test_scaled=X_scaler.transform(X_s_test)

# Fitting the model
from sklearn.linear_model import LogisticRegression

lr = LogisticRegression(solver='lbfgs',random_state=42)
lr.fit(X_s_train_scaled,y_s_train)

LogisticRegression(random_state=42)

predicted_status=lr.predict(X_s_test_scaled)

print(f"The accuracy of the model is : {accuracy_score(y_s_test,predicted_status):.3f}")

The accuracy of the model is : 0.950

print("The confusion matrix is as follows:")
print(confusion_matrix(y_s_test,predicted_status))

The confusion matrix is as follows:
[[ 46  10]
 [ 18 481]]

print("The classification report is as follows:")
print(classification_report(y_s_test,predicted_status))

The classification report is as follows:
              precision    recall  f1-score   support

   Developed       0.72      0.82      0.77        56
  Developing       0.98      0.96      0.97       499

    accuracy                           0.95       555
   macro avg       0.85      0.89      0.87       555
weighted avg       0.95      0.95      0.95       555

Random Forest

Model to predict country's status

from sklearn.ensemble import RandomForestClassifier

rfc = RandomForestClassifier(random_state=42)
rfc.fit(X_s_train_scaled,y_s_train)

RandomForestClassifier(random_state=42)

predicted_status_rf=rfc.predict(X_s_test_scaled)

print(f"The accuracy of the model is : {accuracy_score(y_s_test,predicted_status_rf):.3f}")

The accuracy of the model is : 1.000

print("The confusion matrix is as follows:")
print(confusion_matrix(y_s_test,predicted_status_rf))

The confusion matrix is as follows:
[[ 56   0]
 [  0 499]]

print("The classification report is as follows:")
print(classification_report(y_s_test,predicted_status_rf))

The classification report is as follows:
              precision    recall  f1-score   support

   Developed       1.00      1.00      1.00        56
  Developing       1.00      1.00      1.00       499

    accuracy                           1.00       555
   macro avg       1.00      1.00      1.00       555
weighted avg       1.00      1.00      1.00       555

# Feature importance
sort_list=sorted(zip(X_s.columns,rfc.feature_importances_),key=lambda x: x[1],reverse=True)
labels, values = zip(*sort_list)
indexes = np.arange(len(labels))
width = 1
fig, ax = plt.subplots(figsize=(12, 12))
plt.bar(indexes,values,linewidth=1,tick_label=labels,edgecolor="black")
ax.set(title="Feature Importance",xlabel="Feature",ylabel="Importance")
plt.savefig("../Images/Plots/Bar_Plot.png")

KMeans Clustering

Studying clusters in data

# Reindexing dataframe
df=df.reset_index(drop=True)

# Tranforming country status
onc=OneHotEncoder(sparse=False)

encoded_df=pd.DataFrame(onc.fit_transform(df["status"].values.reshape(-1,1)))
encoded_df.columns=onc.get_feature_names_out(["status"])
encoded_df.tail()

	status_Developed	status_Developing
2212	0.0	1.0
2213	0.0	1.0
2214	0.0	1.0
2215	0.0	1.0
2216	0.0	1.0

# Merging the dataframes
cluster_df=df.join(encoded_df)
cluster_df.head()

	country_code	country	region	status	year	ALC	AMR	BCG	CANP	DTP	...	INCI	INFMR	LE	MCV	OBP	POPD	POPG	SR	status_Developed	status_Developing
0	AFG	Afghanistan	South Asia	Developing	2000	0.002667	310.8305	30.0	0.502297	48.0	...	0.333	90.5	55.841	27.0	2.3	31.829117	2.975057	105.9	0.0	1.0
1	AFG	Afghanistan	South Asia	Developing	2001	0.005333	304.8580	43.0	0.508536	59.0	...	0.318	87.9	56.308	37.0	2.4	33.095904	3.902805	105.9	0.0	1.0
2	AFG	Afghanistan	South Asia	Developing	2002	0.008000	298.8855	46.0	0.512110	62.0	...	0.386	85.3	56.784	35.0	2.6	34.618102	4.496719	105.9	0.0	1.0
3	AFG	Afghanistan	South Asia	Developing	2003	0.010667	292.0365	44.0	0.515965	66.0	...	0.391	82.7	57.271	39.0	2.7	36.272510	4.668344	105.9	0.0	1.0
4	AFG	Afghanistan	South Asia	Developing	2004	0.013333	285.1880	51.0	0.520604	72.0	...	0.389	80.0	57.772	48.0	2.9	37.874413	4.321560	105.9	0.0	1.0

5 rows × 25 columns

# Dropping unwanted columns

cluster_df.drop(columns=["country","year","status","country_code","region"],inplace=True)
cluster_df.head()

	ALC	AMR	BCG	CANP	DTP	EDI	GDP	GDPG	HDI	HE	INCI	INFMR	LE	MCV	OBP	POPD	POPG	SR	status_Developed	status_Developing
0	0.002667	310.8305	30.0	0.502297	48.0	0.235	4.055180e+09	3.868380	0.350	9.0	0.333	90.5	55.841	27.0	2.3	31.829117	2.975057	105.9	0.0	1.0
1	0.005333	304.8580	43.0	0.508536	59.0	0.247	4.055180e+09	3.868380	0.353	9.0	0.318	87.9	56.308	37.0	2.4	33.095904	3.902805	105.9	0.0	1.0
2	0.008000	298.8855	46.0	0.512110	62.0	0.259	4.055180e+09	3.868380	0.384	9.0	0.386	85.3	56.784	35.0	2.6	34.618102	4.496719	105.9	0.0	1.0
3	0.010667	292.0365	44.0	0.515965	66.0	0.271	4.515559e+09	3.868380	0.393	9.0	0.391	82.7	57.271	39.0	2.7	36.272510	4.668344	105.9	0.0	1.0
4	0.013333	285.1880	51.0	0.520604	72.0	0.302	5.226779e+09	-2.875203	0.409	10.0	0.389	80.0	57.772	48.0	2.9	37.874413	4.321560	105.9	0.0	1.0

cluster_df.dtypes

ALC                  float64
AMR                  float64
BCG                  float64
CANP                 float64
DTP                  float64
EDI                  float64
GDP                  float64
GDPG                 float64
HDI                  float64
HE                   float64
INCI                 float64
INFMR                float64
LE                   float64
MCV                  float64
OBP                  float64
POPD                 float64
POPG                 float64
SR                   float64
status_Developed     float64
status_Developing    float64
dtype: object

# Scaling the data
cluster_scaled=MinMaxScaler().fit_transform(cluster_df)

# Applying PCA to reduce dimensions from 21 to 4

from sklearn.decomposition import PCA

pca=PCA(n_components=4)
cluster_pca=pca.fit_transform(cluster_scaled)

# Checking the explained variance ratio - Variables will be adjusted to use fewer parameters in next iteration
pca.explained_variance_ratio_

array([0.53456714, 0.20126763, 0.0589989 , 0.05359823])

# Adding pca values to df

pca_df=pd.DataFrame(cluster_pca,columns=["PC1","PC2","PC3","PC4"])

# Identifying best value for k

from sklearn.cluster import KMeans

inertia=[]
k = list(range(1,11))

# Looping through definined k options
for value in k:
    km = KMeans(n_clusters=value,random_state=42)
    km.fit(pca_df)
    inertia.append(km.inertia_)
    
# Elbow curve
elbow_df=pd.DataFrame({"k":k,"inertia":inertia})

elbow_df.plot(x="k",y="inertia",xlabel="Number of clusters",ylabel="Inertia")

C:\Users\DCANOWERKCOMPUTA\anaconda3\envs\mlenv\lib\site-packages\sklearn\cluster\_kmeans.py:1037: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=9.
  "KMeans is known to have a memory leak on Windows "

<AxesSubplot:xlabel='Number of clusters', ylabel='Inertia'>

# KMeans Clustering

kmodel=KMeans(n_clusters=3,random_state=42)
kmodel.fit(pca_df)

KMeans(n_clusters=3, random_state=42)

# Predicting the cluster

cluster=kmodel.predict(pca_df)

pca_df["class"]=kmodel.labels_
pca_df.head()

	PC1	PC2	PC3	PC4	class
0	-1.128039	0.714938	0.684560	0.105317	0
1	-1.053151	0.611978	0.480666	0.029218	0
2	-0.988180	0.566578	0.493590	0.035084	0
3	-0.946992	0.532654	0.463901	0.020235	0
4	-0.867272	0.453745	0.333850	-0.006954	0

combined_df=df.join(pca_df)
combined_df.head()

	country_code	country	region	status	year	ALC	AMR	BCG	CANP	DTP	...	MCV	OBP	POPD	POPG	SR	PC1	PC2	PC3	PC4	class
0	AFG	Afghanistan	South Asia	Developing	2000	0.002667	310.8305	30.0	0.502297	48.0	...	27.0	2.3	31.829117	2.975057	105.9	-1.128039	0.714938	0.684560	0.105317	0
1	AFG	Afghanistan	South Asia	Developing	2001	0.005333	304.8580	43.0	0.508536	59.0	...	37.0	2.4	33.095904	3.902805	105.9	-1.053151	0.611978	0.480666	0.029218	0
2	AFG	Afghanistan	South Asia	Developing	2002	0.008000	298.8855	46.0	0.512110	62.0	...	35.0	2.6	34.618102	4.496719	105.9	-0.988180	0.566578	0.493590	0.035084	0
3	AFG	Afghanistan	South Asia	Developing	2003	0.010667	292.0365	44.0	0.515965	66.0	...	39.0	2.7	36.272510	4.668344	105.9	-0.946992	0.532654	0.463901	0.020235	0
4	AFG	Afghanistan	South Asia	Developing	2004	0.013333	285.1880	51.0	0.520604	72.0	...	48.0	2.9	37.874413	4.321560	105.9	-0.867272	0.453745	0.333850	-0.006954	0

5 rows × 28 columns

combined_df.hvplot.scatter(x="PC1",y="PC2",by="class",hover_cols=["country","year"])

class_df=combined_df.groupby(["region","class"])[["region"]].count()
print(class_df.to_string())

                                  region
region                     class        
East Asia & Pacific        0          99
                           1         235
                           2          25
Europe & Central Asia      0           5
                           1         261
                           2         208
Latin America & Caribbean  0          16
                           1         368
Middle East & North Africa 1         184
South Asia                 0          78
                           1          50
Sub-Saharan Africa         0         599
                           1          89

combined_df[["country","class"]].value_counts()

country                           class
Afghanistan                       0        16
Mauritania                        0        16
Panama                            1        16
Pakistan                          0        16
Oman                              1        16
                                           ..
Kenya                             1         2
Azerbaijan                        0         2
Cambodia                          1         2
Kiribati                          0         1
Micronesia (Federated States of)  0         1
Length: 159, dtype: int64